Academic training in analytics emphasizes mathematical and programming skills, and with good reason. The trouble is that many people who have outstanding mathematical and programming skills end up producing lackluster results in the working world. One reason: lousy listening skills.

Technical skills are important, of course, and not easy to develop. Textbook problems are mighty hard to solve when you’re first learning analytics methods. After graduation, though, additional complexities come into the mix, complexities that most graduates are not adequately prepared to handle.

Real-life analytics problems come buried within a mass of everyday business communication. Executives, customers and other non-analysts describe problems in terms that don’t obviously connect to analytics. They don’t clearly explain what they want from analytics, because they don’t know. After all, that’s not their area of expertise.

Interest in better communication for data analysts is rising. It’s included in the cute Venn diagrams of skills that are popular in the data science community. It’s the topic of an online class created by University of Washington. It’s a hot topic in Harvard Business Review. In these, and most of the currently-circulating material on analytics communication, the emphasis is on sharing results. Words like “explaining,” “visualization,” and “storytelling” appear often. It’s certainly true that a good data analyst must be able to clearly explain results. But why is so little attention paid to the even more important skill of listening?

The most valuable data analysts are not the most dazzling mathematicians, programmers or storytellers; they’re the best listeners.

By “listeners,” I don’t mean people who believe everything that’s said to them, or who do what’s asked without question. I’m talking about critical thinking skills. Words are important, but words don’t always match perfectly to meaning or action. Superior listeners gather more information than just the words that people say, and use all of that information to infer meaning.

Let me give a little example from everyday life. I recently attended a routine meeting with a group of business men. At the end, the leader asked some questions, including, “How do you like this location for the meeting?” Everybody else said the location was great. I said it was not great. He did one thing right, by asking why I didn’t like it. The problem was, he didn’t understand my concerns. I mentioned that I travel on public transit. He brushed off the response, naming the transit stops within a few blocks. I pointed out that the entrance to the building was awkward, and that it would be problematic during bad weather. He brushed that off, too.

He wasn’t using nonverbal clues to meaning: my style of communication (less direct than his), my gender (I was the only woman in the room), the fact that I lived nearby (so I already knew the locations of transit stops). All of these things contributed to the real meaning of my words. If I had been truly direct, I would have said, “The entrance to this building is not a safe place, you @#$%^!&.” But I don’t talk like that, at least not during business meetings.

People say things in indirect ways. To interpret meaning, you have to use more than the words they say, and that’s a fundamental issue for providing valuable analytics. You must develop keen listening skills in order to assess business problems and define analytics project plans that address underlying concerns. If you fail to draw out those concerns, the rest of your work won’t matter.

It might seem that listening is a skill learned in classroom settings. After all, most of your time in the classroom is spent listening to the lecturer speaking, or students asking or answering questions. But classroom listening is not like business listening. Business talk is loaded with hidden agendas, conflicting interests and competing viewpoints that are not found in the classroom.

So how can data analysts refine listening skills? Here are a three simple ways to practice:

Scope real business problems.

Many analysts learn primarily by solving problems assigned by teachers, or defined by the analysts themselves in self-study. Even many professional analysts today have limited contact with their true constituents, the business managers and customers who experience a problem directly. Look for opportunities to speak directly to the people affected by a problem and hear what they have to say about it.

Attend meetings.

If you think meetings are a waste of time, you’re doing meetings wrong. Listen carefully, and observe what’s going on around you. You’ll find that even the dullest meeting is a rich source of information that reveals details of what’s going on within an organization. What are people saying? How do they say it? What’s the body language? Who are the influencers and how are they influencing others? Are the actions you observe consistent with the words you hear?

Document.

Before you begin any analysis, document the problem. Write down what you know. Organize the important points into a single page document. Make it the clearest and best organized explanation you can, using full sentences and paragraphs. Then share it with your manager or client. Ask for feedback. Is your understanding of the problem correct? Have you missed or misunderstood any points? Should you research anything further?

A clear understanding of the business problem and related issues is absolutely basic to successful analytics. You have to fully understand a problem to develop a meaningful solution, and show that your results are truly relevant to the business. That’s why every successful analytics project starts with patient, thoughtful listening.

This article was written by Meta S. Brown from Forbes and was legally licensed through the NewsCred publisher network.